TY  - JOUR
T1  - IMPEDANCE IMAGE RECONSTRUCTION WITH ARTIFICIAL NEURAL NETWORK IN ELECTRICAL IMPEDANCE TOMOGRAPHY
AU  - Kilic, Beyhan
PY  - 2019
DA  - December
DO  - 10.36222/ejt.650616
JF  - European Journal of Technique (EJT)
JO  - EJT
PB  - Hibetullah KILIÇ
WT  - DergiPark
SN  - 2536-5010
SP  - 137
EP  - 144
VL  - 9
IS  - 2
LA  - en
AB  - Electricalimpedance tomography views the electrical properties of the objects byinjecting current with surface electrodes and measuring voltages. Then using areconstructing algorithm, from the measured voltage-current values,conductivity distribution of the object calculated. Finding internalconductivity from surface voltage-current measurements is a reverse andill-posed problem.Therefore,high error sensitivity, and making approximations in conceiving complexcomputations cause to limited spatial resolution. The classic iterative imagereconstruction algorithms have reconstruction errors. Accordingly, Electricalimpedance tomography images suffer low accuracy. It is necessary to evaluatethe collected data from the object surface with a new approach. In this paper,the forward problem solved with the finite element method to reconstruct theconductivity distribution inside the object,the reverse problem solved by the neural network approach. Image reconstructionspeed, conceptual simplicity, and ease of implementation maintained by this approach.
KW  - : electrical impedance tomography
KW  - finite element methods
KW  - biomedical  image reconstruction
KW  - neural network
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UR  - https://doi.org/10.36222/ejt.650616
L1  - https://dergipark.org.tr/en/download/article-file/911333
ER  -